


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A						={
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						=["AlbertTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						=["AlbertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						=[
		    "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "AlbertForMaskedLM",
		    "AlbertForMultipleChoice",
		    "AlbertForPreTraining",
		    "AlbertForQuestionAnswering",
		    "AlbertForSequenceClassification",
		    "AlbertForTokenClassification",
		    "AlbertModel",
		    "AlbertPreTrainedModel",
		    "load_tf_weights_in_albert",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						=[
		    "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFAlbertForMaskedLM",
		    "TFAlbertForMultipleChoice",
		    "TFAlbertForPreTraining",
		    "TFAlbertForQuestionAnswering",
		    "TFAlbertForSequenceClassification",
		    "TFAlbertForTokenClassification",
		    "TFAlbertMainLayer",
		    "TFAlbertModel",
		    "TFAlbertPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						=[
		    "FlaxAlbertForMaskedLM",
		    "FlaxAlbertForMultipleChoice",
		    "FlaxAlbertForPreTraining",
		    "FlaxAlbertForQuestionAnswering",
		    "FlaxAlbertForSequenceClassification",
		    "FlaxAlbertForTokenClassification",
		    "FlaxAlbertModel",
		    "FlaxAlbertPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

		__A						=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






